"In 1950 appeared the first edition of Oskar Morgenstern's famous book, The Accuracy of Economic Observations. Nearly half a century later it is timely to return to Morgenstern's diagnosis and to contemplate his therapeutic recommendations. Morgenstern's vision can and should inform the consideration of the topic today because of the continued validity of many of his findings. His work still provides stimuli for studying the general problems of measurement, the varying requirements for accuracy, the issues of aggregate macroeconomic measures, and the prospects for economic and social measurement. This is so even if some of the bleaker assessments by Morgenstern, notwithstanding their technical merits, provide little or no practical guidance for statistical activities. In this context it is enlightening to recall the different practical attitudes adopted by Keynes and by some of his contemporaries in Germany regarding theoretical difficulties with aggregate macroeconomic data."

"Quantile regression, as introduced by Koenker and Bassett (1978), may be viewed as an extension of classical least squares estimation of conditional mean models to the estimation of an ensemble of models for several conditional quantile functions. The central special case is the median regression estimator which minimizes a sum of absolute errors. Other conditional quantile functions are estimated by minimizing an asymmetrically weighted sum of absolute errors. Quantile regression methods are illustrated with applications to models for CEO pay, food expenditure, and infant birthweight."

"Federal statistical agencies in the United States and analogous agencies elsewhere commonly report official economic statistics as point estimates, without accompanying measures of error. Users of the statistics may incorrectly view them as error free or may incorrectly conjecture error magnitudes. This paper discusses strategies to mitigate misinterpretation of official statistics by communicating uncertainty to the public. Sampling error can be measured using established statistical principles. The challenge is to satisfactorily measure the various forms of nonsampling error. I find it useful to distinguish transitory statistical uncertainty, permanent statistical uncertainty, and conceptual uncertainty. I illustrate how each arises as the Bureau of Economic Analysis periodically revises GDP estimates, the Census Bureau generates household income statistics from surveys with nonresponse, and the Bureau of Labor Statistics seasonally adjusts employment statistics. I anchor my discussion of communication of uncertainty in the contribution of Oskar Morgenstern (1963a), who argued forcefully for agency publication of error estimates for official economic statistics."

The technology to estimate them and predict from them quickly now exists. It's true that if you fit a linear regression and a non-parametric regression to the same data set, the linear regression will always have tighter confidence sets, but (as Jeffrey Racine says) that's rapid convergence to a systematically wrong answer.